Federal AI Intelligence
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Snapshot · OMB 2025 inventory · processed Oct 11, 2026
Federal AI landscape

Federal AI at a glance

10 agencies · 32 reported use cases · OMB 2025 Inventory. Portfolio size reflects reporting, not performance.

Executive briefing ↗Explore 32 use cases →
Agency
Stage
High-impact
Topic
AI type1
Sourcing1
Vendor
No options match.
Function1
Start year

Only records with a parseable start date match a year filter.

AI type: Classical / predictive MLFunction: Forecasting and predictionSourcing: Developed in-house
32 Reported AI use cases10 agencies15 Deployed14 Pilots3 Designated high-impact
Decorative federal AI network visualization; not a representation of agency deployment locations or relationships.
Scale & composition

Agencies behind federal AI

Select an agency to explore
Ring shows composition · node area shows use cases
DOI12DHS5
In development/acquisition2Pilot14Deployed15Retired1Largest portfolio: 12 · 10 agencies

Conceptual agency landscape · positions do not represent deployment locations. Proximity does not imply a relationship.

AI technology mix

Reported classification · click to cross-filter

0% generative or agentic. Each record carries one classification; not reported is shown hatched.

Mission areas

Topic area as reported

Lifecycle composition

32 records
Explore lifecycle stages and impact
Within each stageHigh-impact · Gen./agentic
Pre-deployment0%0%
Pilot7%0%
Deployed13%0%
Retired0%0%

Impact and technology shares use each stage's records as the denominator.

From development and acquisition to retirement. Select a stage to explore its reported use cases.

Reading the inventory

Evidence, with its limits visible

DOI, DHS, HHS report 20 of 32 use cases (63%).

Understand the methodology →

Evolution of reported AI use cases

By agency-reported start year · click a year to filter
05<15'15'16'17'18'19'20'21'22'238'24'25'26
Generative or agenticOther or unclassified30 of 32 records report a parseable start date

Start dates are agency-reported operational or pilot dates, not inventory submission dates. 2026 values are planned or early-year. Undated records are excluded from this chart only.

Sourcing

Reported for 100% of records

Each square is 1% of filtered records. Where reported, 100% involve in-house development.

High-impact AI

Agency self-designation under M-25-21Governance view
32records
Minimum practices · 3 high-impactin place
Pre-deployment testing33%
AI impact assessment33%
Independent review33%
Ongoing monitoring33%
Operator training33%
Fail-safe33%
Appeal process0%
User and public consultation0%
Reported in placeNot applicableOther or ambiguous answerNo answer reported

High-impact is the agency's own classification, not an independent risk rating. No answer reported is missing evidence, not evidence that a practice is absent. 33% of high-impact records answer any practice question.

Analytical observations

Computed from the current filter; reproducible
  • FindingDOI, DHS, HHS report 20 of 32 use cases (63%).
  • Finding0% of reported deployed use cases (0 of 15) are classified as generative or agentic AI. View records
  • FindingOf 2 deployed high-impact use cases, 1 report a completed AI impact assessment and 1 leave every minimum-practice field blank. View records
  • InterpretationBlank practice fields are missing evidence, not evidence that a safeguard is absent. They mark where follow-up questions to the agency are most useful.
  • FindingSourcing is reported for 100% of use cases. Where reported, 100% involve in-house development.
AI classification reported100%
Stage not reported0
Sourcing reported100%